The reference to the prior art in the following discussion is not to be taken as any representation or admission that such art forms part of the common general knowledge. The disclosures of each of the publications referred to herein are hereby incorporated by reference in their entireties and for all purposes.
Obstructive sleep apnea hypopnea syndrome (OSAHS) is a serious sleep disorder with high prevalence among the population [1, 2]. In the USA, about 24% of men and 9% of women fall within Medicare guidelines [1] for treatment. In Singapore 15% of the total population is at risk [3]. Over 1.2 million Australians experience sleep disorders costing the country $10.3 billion (in 2004) [4]; OSAHS is the commonest disorder (66% of the total).
OSAHS is characterized by breathing interruption during sleep. Full closure of the airways is known as obstructive Apnea, and a partial closure is defined as obstructive Hypopnea (See Appendix A for technical definitions). The common symptoms of OSAHS are excessive daytime sleepiness and intermittent snoring [5, 6].
OSAHS is a major risk factor for downstream complications such as stroke, diabetes and cardiovascular disease [6, 7]. It is also known to be associated with cognitive deficiencies, low IQ in children, fatigue and accidents. It is responsible [7] for 11,000-43,000 traffic accidents per year in NSW. Untreated patients are known to utilize twice the national health resources prior to diagnosis [8]. OSAHS is treatable. If diagnosed early, its devastating secondary complications can be thwarted. However, over 90% individuals with OSAHS are estimated to be undiagnosed at present [2].
The standard test for OSAHS diagnosis is Polysomnography (PSG) [9]. PSG is a technique to monitor multiple neuro-physiological and cardio respiratory signals, over the course of night. It requires a full-night sleep-laboratory stay in a specifically equipped sleep-suite, connected to over 15 channels of measurements. The 6-8 hours of sleep data is then subjected to a complex and time-consuming manual process (Sleep Scoring) to identify events of Apnea/Hypopnea and a type of sleep a disturbance known as EEG-arousals (EEGA). The outcomes of PSG test are summary measures of OSAHS severity such as the Respiratory Disturbance Index (RDI) and the EEG Arousal index (ArI) etc (please see appendix B for details).
EEG in the Diagnosis of Sleep Disorders
Sleep Scoring and Macro-Sleep-Architecture
Sleep is essentially a neuropsychological phenomenon; EEG still remains the cheapest and the most portable technique for the functional imaging of the brain during sleep. It is also the technique with the highest temporal resolution available. In the current practice of PSG Scoring, EEG is regarded as an indispensable signal when a definitive diagnosis is desired. Thus, in-facility diagnostic PSG tests always include EEG. Electromyography (EMG) and Electroocculography (EOG) signals are also needed for the correct EEG-centred interpretation of sleep states.
In diagnostic PSG tests EEGs are essential for the following tasks:                (i) to define EEG-arousals and identify sleep fragmentation. EEG-arousals are also used as one parameter in defining Hypopneas (see Appendix A).        (ii) to score the Macro Sleep Architecture (MSA) of sleep. It is a process in which sleep is classified into three macro states: (1) Wake State (SW), (2) Rapid Eye Movement (REM) Sleep State (SR), and (3) Non-REM Sleep State (SN). The MSA is extremely important in the diagnosis of OSAH. In addition, Sleep MSA may be used in the diagnosis/monitoring of a range of sleep disorders including Narcolepsy, Insomnia, Sudden Infant Death Syndrome and Depression etc.                    Some important uses of MSA in PSG includes:            (a) Estimating the Total Sleep Time, TST, which is needed for the computation of the RDI index. The TST is also useful as a summary indicator of the quality of sleep during a PSG test. Note that the TST, which is defined using EEG, can be significantly different from the Total Time in Bed (TTB) measured with a clock.            (b) Estimating clinically important descriptors of sleep such as the Sleep Efficiency (SE), Sleep Latency (SL), REM Latency (RSL), the total time spent in REM, and the percentage of time spent in REM.            (c) Expressing most of the clinically relevant sleep parameters separately for REM and NREM sleep, before providing an overall number. Some Examples are: the Arousal Index in REM sleep, The Arousal Index in NREM sleep, RDI in REM sleep and RDI index in NREM sleep. The reason behind this is that the REM/NREM classification provides fundamental information about sleep and its diagnostic characteristics.                        
An EEG may be broadly divided into four major frequency bands [10], Delta (δ, 0.1-4 Hz), Theta (θ, 4.1-8 Hz), Alpha (α, 8.1-12 Hz), and Beta (β, >12.1 Hz). FIG. 1 shows the EEG activity at different states, (a) awake drowsy state, (b) light sleep (NREM sleep Stage 1 and Stage 2), (c) deep sleep (NREM sleep Stage 3 and Stage 4) and (d) REM sleep. These frequency bands are heavily used in sleep scoring.
The scoring of MSA is done manually using the rules laid down by Rechtschaffen and Kales (R&K, 1968) (see appendix C for a summary) [11]. Manual scoring relies on visual extraction of specific features in two EEG channels (usually C3-A2 and C4-A1 of the International 10/20 system), two channels each of EMG and EOG. Thus, six channels of electrophysiological data have to be visually interpreted, simultaneously taking care of difficulties such as measurement artifacts.
This process is time consuming (typically 1-2 hours per patient), costly (hundred of dollar per recording) and prone to inter and intra scorer variability [12-15]. The scorers from different laboratories tend to agree less than scorers from the same laboratories, due to differences in interpretation and subjective implementations. For example, the mean epoch by epoch agreement between the scorers from three sleep laboratories in the USA for healthy subjects is 76% (range 65-85%) which decreases to 71% (range 65-78%) in the OSAHS cases [16]. A similar result (76.8%) has been reported by European laboratories based on a large database of 196 recordings from 98 patients [13].
The accurate computation of sleep parameters such as the RDI, ArI and REM Latency is important for the clinical diagnosis of a range of sleep disorders. Thus, the final diagnostic accuracy heavily depends on the precise scoring of MSA. However, due to the subjectivity associated with the scoring process there exists a significant variability in the PSG results between the technicians of the same laboratory and across the different sleep laboratories [14, 15]. For example, [12] reported that a patient can get two different diagnoses in two different laboratories which might range from as low as RDI=4.9 to as high as RDI=79.
In order to overcome the problems associated with manual scoring and cater to the ever increasing demand for PSG testing, several researchers have proposed automatic sleep scoring systems [17-22]. After publication of R&K's rules in 1968, several authors tried to automate them and achieved various degrees of agreement with human scorers. With the advancement in digital signal processing techniques, several other used frequency spectral analysis [22], neural network analysis [18, 20], multidimensional scaling and wavelets techniques or expert system approaches [21] to develop automatic sleep staging systems. However, a reliable and accurate method with sufficient precision suitable for in-facility PSG as well as other take-home OSAHS screening devices does not exist yet. Despite the inherent subjective nature, human scoring is still considered the golden method of MSA scoring. Existing methods for automatic MSA scoring have the following shortcomings:                1. The R&K rules depend on visual features in sleep EEG and were originally proposed specifically for manual scoring. Most of the automated techniques try to implement R&K rules and depend on morphological features like k-complexes, vertex-waves and spindles. These characteristics are severely altered in disease states such as OSAHS [6, 10, 23, 24]. Consequently their performances decreases in OSAHS [19, 25]. In addition, the detection of visual features is a highly subjective process.        2. The agreement between automatic and human classifications is smaller than the agreement between human scorers [25]. This result is to be expected because the R&K criteria are based on visual features and humans are better than machines in visual pattern recognition.        3. The differentiation of REM/NREM/WAKE is critical in OSAHS diagnosis. However, automated methods had difficulties in distinguishing wake state from Stage 1 of NREM sleep and REM sleep.        4. Automated scoring techniques currently available in PSG equipment need expert human intervention for manually editing the outcomes, and thus are not truly automated systems [25]. The human intervention makes them subjective and time consuming.        5. Existing methods have not been tested under disease conditions such as OSAHS, Periodic Leg Movement Syndrome (PLMS) or upper airway respiratory syndrome (UARS), where sleep is corrupted with frequent EEG arousals, apnea events, and recording artifacts.        6. All existing techniques depend on recording multiple physiological signals, making them unsuitable for portable monitors used for OSAHS screening.        
The AASM definition [26] of micro-sleep is: “ . . . an episode lasting up to 30 seconds during which external stimuli are not perceived. The PSG suddenly shifts from waking characteristics to sleep”. It is generally believed that micro-sleep is closely associated with excessive diurnal sleepiness. Excessive daytime sleepiness and spontaneous micro-sleep are two major consequences of OSAHS [27], contributing to motor vehicle and work related accidents. It is estimated to affect 12% of the adult population [27].
Clinically, sleepiness is commonly expressed by the measure Sleep Latency (SL), which is the length of time required to fall asleep. The common tests for measuring SL are Multiple Sleep Latency Test (MSLT) and Maintenance of Wakefulness Test (MWT), technical details for which are provided in Appendix E. In these tests SL is computed as the time from the start of recording to the sleep onset. To technically identify sleep onset, sleep technician have to simultaneously look at multiple signals. It is a tedious and a subjective process resulting in high inter-rater, as well as intra-rater, variability [12]. The SL also provides valuable information in the diagnosis of other widespread diseases such as insomnia.
Even though diurnal micro-sleep is an important phenomenon related to sleep disturbances, there is no objective system of measurements to detect micro-sleep or express its severity. In routine PSG tests targeted for OSAHS diagnosis, episodes of micro-sleeps are not scored.
It is an object of the present invention to provide a method and apparatus that addresses one or more of the various problems discussed above in relation to prior art methods for determining sleep-related parameters.